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All right, guys.

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So now I will test my model on a live webcam.

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So I have downloaded the best weights file from here.

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So I will open PyCharm and write down the strip to test my model on the live webcam.

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So I am just opening PyCharm and it might take few seconds.

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So the PyCharm has opened.

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Now I will go to file and create a new project, so I will click on file from here and create a new

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project.

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And from here I will select the location.

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And from here this is in my book.

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So here I will select this folder to test my model on the live webcam.

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So I will select the live webcam folder and click on.

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Okay.

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And then I will.

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Environment directory is not empty.

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Okay, so let us create a new folder over here.

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Webcam testing.

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Okay.

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I have created a folder by the name webcam testing and in the light webcam I have placed my best weights

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file and prediction, so I will just copy those two files from here and paste in the webcam testing.

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So best weights file is the my model weights file.

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Best fit is by basically my model weights file and predictions.py the script to test my model on the

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live webcam.

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Okay.

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So just opening PyCharm and just selecting this folder.

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So webcam testing.

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Okay.

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And creating a new project.

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So selecting this window and let's see what results do we get.

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Okay, so it's creating a virtual environment.

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So opening predictions.py.

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So in the first step I am importing YOLO from the ultralytics, then importing v2.

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So in the in this model variable name I am calling YOLO weights and YOLO model and passing the best

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paid weights file.

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So this here we have the predict method.

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So predict methods.

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Take all the parameters of the command line interface.

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So as I'm testing my model on the live webcam of my laptop so I have selected source zero and show is

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equal to true means because I want to display the results live and I have selected the confidence value

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as 0.15.

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So model will display all the predictions or the bounding boxes where we have confidence value till

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0.5 below confidence value 0.15.

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The model will not display bounding boxes or labels or you can say predictions.

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So let's test our model on the webcam.

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So I will write over here.

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I thought.

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Their addictions.

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Dot.

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I and click on Enter and see what results do we get.

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So.

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It might take a few seconds to open the webcam.

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So then see what results do we get?

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Okay, so here I have the resistor in front of me.

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Or just I'm just going back with to back.

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So.

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So our model is you can see that detecting that there is a bug.

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So.

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In case a book.

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It's working fine.

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Okay, so one model is able to detect there is a book.

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So let's me pick up my another register and see what results do we get over here.

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So here it's also detecting that there is a book.

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So our model is detecting book verifying like if our model is able to detect that there is a book while

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in case of ballpoint, we will see that whether restricting or not.

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Okay.

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In case of ballpoint point, our strategic predictions are not very fine.

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Like our model is unable to detect it.

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Have only single ballpoint at this time, so can just test it over from this while in case of work.

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Our model works very fine and it is able to detect that there is a book you can see over here.

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So let's let's see you on in the next project.

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Till then, bye bye.

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See you all in the next video.